Upload folder using huggingface_hub

best_models.csv

@@ -1,7 +1,7 @@
-sku,best_model,characteristic
-Item_A,prophet,continuous
-Item_B,prophet,continuous
-Item_C,fft_i,continuous
-Item_D,fft_i,continuous
-Item_E,prophet,continuous
-Item_F,ceif,fuzzy
+sku,best_model,characteristic,predictability,RMSE,Intermittent Scores
+Item_A,,,,,
+Item_B,,,,,
+Item_C,,,,,
+Item_D,,,,,
+Item_E,,,,,
+Item_F,,,,,

demo.py

@@ -41,7 +41,7 @@ with demo:
 
     md_ts_data_info = gr.Markdown()
 
-    df_ts_data = gr.DataFrame(**args.df_ts_data)
+    df_ts_data = gr.Dataframe(**args.df_ts_data)
 
     with gr.Accordion('Input Data Visualisation', open=False):
         dropdown_ts_data = gr.Dropdown(**args.dropdown_ts_data)
@@ -68,7 +68,7 @@ with demo:
             gr.Markdown('Upload previous model selection result (if have):')
             file_upload_model_data = gr.File(**args.file_upload_model_data)
 
-    df_model_data = gr.DataFrame()
+    df_model_data = gr.Dataframe()
     file_model_data = gr.File()
 
     accordion_model_selection = gr.Accordion(

demo2

@@ -0,0 +1,275 @@
+import json
+
+import gradio as gr
+from gr_app2 import args, GradioApp
+
+demo = gr.Blocks(**args.block)
+
+with demo:
+    app = GradioApp()
+
+    md__title = gr.Markdown(**args.md__title)
+
+    with gr.Row():
+        with gr.Column():
+            file__historical = gr.File(**args.file__historical)
+
+        with gr.Column():
+            file__future = gr.File(**args.file__future)
+            md__future = gr.Markdown(**args.md__future)
+
+    with gr.Row():
+        btn__load_historical_demo = gr.Button("Load Demo Historical Data")
+        btn__load_future_demo = gr.Button("Load Demo Future Data")
+
+    with gr.Row():
+        number__n_predict = gr.Number(
+            value=app.n_predict,
+            **args.number__n_predict)
+
+        number__window_length = gr.Number(
+            value=app.window_length,
+            **args.number__window_length
+        )
+
+        textbox__target_column = gr.Textbox(
+            value=app.target_column,
+            **args.textbox__target_column
+        )
+
+        number__n_predict.change(
+            app.number__n_predict__change,
+            [number__n_predict]
+        )
+
+        number__window_length.change(
+            app.number__window_length__change,
+            [number__window_length]
+        )
+
+        textbox__target_column.change(
+            app.textbox__target_column__change,
+            [textbox__target_column]
+        )
+
+    # ---------- #
+    # Data Views #
+    # ---------- #
+
+    with gr.Tabs():
+        with gr.Tab('Table View'):
+            df__table_view = gr.Dataframe(**args.df__table_view)
+
+        with gr.Tab('Chart View'):
+            dropdown__chart_view_filter = gr.Dropdown(
+                multiselect=True,
+                label='Filter')
+            plot__chart_view = gr.Plot()
+
+        with gr.Tab('Seasonality and Auto Correlation'):
+            dropdown__seasonality_decompose = gr.Dropdown(
+                label='Please select column to decompose')
+
+            with gr.Row():
+                plot__seasonality_decompose = gr.Plot()
+                plot_acg_pacf = gr.Plot()
+
+        with gr.Tab('Correlations'):
+            btn__plot_correlation = gr.Button('Plot Correlations')
+            plot__correlation = gr.Plot()
+
+            btn__plot_correlation.click(
+                app.btn__plot_correlation__click,
+                [],
+                [plot__correlation]
+            )
+
+        with gr.Tab("Data Profile"):
+            btn__profiling = gr.Button('Profile Data')
+            md__profiling = gr.Markdown()
+            plot__change_points = gr.Plot()
+
+        dropdown__seasonality_decompose.change(
+            app.dropdown__seasonality_decompose__change,
+            [dropdown__seasonality_decompose],
+            [plot__seasonality_decompose,
+             plot_acg_pacf]
+        )
+
+        btn__profiling.click(
+            app.btn__profiling__click,
+            [],
+            [md__profiling,
+             plot__change_points]
+        )
+
+    # ---------------------- #
+    # Fit data to forecaster #
+    # ---------------------- #
+
+    btn__fit_data = gr.Button(**args.btn__fit_data)
+
+    # =========== #
+    # Forecasting #
+    # =========== #
+
+    column__models = gr.Column(visible=True)
+
+    with column__models:
+        md__fit_ready = gr.Markdown(**args.md__fit_ready)
+
+        md__forecast_data_info = gr.Markdown()
+
+        # ------------- #
+        # Model Configs #
+        # ------------- #
+
+        with gr.Row():
+            checkbox__round_results = gr.Checkbox(
+                app.round_results,
+                label='Round Results',
+                interactive=True)
+
+            checkbox__round_results.change(
+                app.checkbox__round_results__change,
+                [checkbox__round_results],
+                []
+            )
+
+        gr.Markdown('## Models')
+        # ------- #
+        # XGBoost #
+        # ------- #
+        with gr.Tab('XGBoost'):
+
+            with gr.Row():
+                checkbox__xgboost_cv = gr.Checkbox(
+                    app.xgboost_cv, label='Cross Validation')
+                checkbox__xgboost_round = gr.Checkbox(
+                    app.xgboost.round_result,
+                    label='Round Result',
+                    interactive=True)
+
+                checkbox__xgboost_round.change(
+                    app.checkbox__xgboost_round__change,
+                    [checkbox__xgboost_round],
+                    []
+                )
+
+            with gr.Row():
+                with gr.Column():
+                    textbox__xgboost_params = gr.Textbox(
+                        interactive=True,
+                        value=app.xgboost_params)
+
+                    btn__set_xgboost_params = gr.Button("Set Params")
+
+                json_xgboost_params = gr.JSON(
+                    value=app.xgboost_params,
+                    **args.json_xgboost_params)
+
+                btn__set_xgboost_params.click(
+                    app.btn__set_xgboost_params__click,
+                    [textbox__xgboost_params],
+                    [json_xgboost_params])
+
+            btn__train_xgboost = gr.Button("Forecast with XGBoost Model")
+
+            plot__xgboost_result = gr.Plot()
+
+            with gr.Row():
+                df__xgboost_result = gr.Dataframe()
+                file__xgboost_result = gr.File()
+
+            btn__train_xgboost.click(
+                app.btn__train_xgboost__click,
+                [],
+                [plot__xgboost_result,
+                 file__xgboost_result,
+                 df__xgboost_result])
+
+        # ------- #
+        # Prophet #
+        # ------- #
+        with gr.Tab('Prophet'):
+            gr.Markdown('Prophet')
+
+            btn__forecast_with_prophet = gr.Button(
+                **args.btn__forecast_with_prophet)
+
+            plot__prophet_result = gr.Plot()
+
+            with gr.Row():
+                df__prophet_result = gr.DataFrame()
+                file__prophet_result = gr.File()
+
+        btn__forecast_with_prophet.click(
+            app.btn__forecast_with_prophet__click,
+            [],
+            [plot__prophet_result,
+             file__prophet_result,
+             df__prophet_result]
+        )
+
+    # --------- #
+    # Operators #
+    # --------- #
+
+    file__historical.upload(
+        app.file__historical__upload,
+        [file__historical],
+        [df__table_view,
+         dropdown__chart_view_filter,
+         dropdown__seasonality_decompose,
+         plot__chart_view])
+
+    file__future.upload(
+        app.file__future__upload,
+        [file__future],
+        [df__table_view,
+         dropdown__chart_view_filter,
+         dropdown__seasonality_decompose,
+         plot__chart_view,
+         number__n_predict])
+
+    btn__fit_data.click(
+        app.btn__fit_data__click,
+        [],
+        [
+            number__n_predict,
+            number__window_length,
+            file__historical,
+            file__future,
+            btn__fit_data,
+            column__models,
+            btn__load_historical_demo,
+            btn__load_future_demo,
+            md__forecast_data_info,
+        ])
+
+    btn__load_historical_demo.click(
+        app.btn__load_historical_demo__click,
+        [],
+        [df__table_view,
+         dropdown__chart_view_filter,
+         dropdown__seasonality_decompose,
+         plot__chart_view,]
+    )
+
+    btn__load_future_demo.click(
+        app.btn__load_future_demo__click,
+        [],
+        [df__table_view,
+         dropdown__chart_view_filter,
+         dropdown__seasonality_decompose,
+         plot__chart_view,
+         number__n_predict]
+    )
+
+    dropdown__chart_view_filter.change(
+        app.dropdown__chart_view_filter__change,
+        [dropdown__chart_view_filter],
+        [plot__chart_view]
+    )
+
+demo.launch()

gr_app/GradioApp.py

@@ -26,7 +26,7 @@ class GradioApp():
 
         self.skus = self.ts_data['sku'].unique().tolist()
 
-        self.model_data = pd.DataFrame(
+        self.model_data = pd.DataFame(
             {
                 'sku': self.skus,
                 'best_model': '',